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International Journal of Wireless Networks and Communications.
ISSN 0975-6507 Volume 11, Number 1 (2019), pp. 1-11
© International Research Publication House
http://www.irphouse.com
Resource Management in 5G Heterogeneous Network
Greeshma S.L.
Sreelekshmi Unnithan
Jissa Anna Thomas
Joylean Anna Kingston
Swapna P.S.
Department of Electronics and Communication Engineering,
Mar Baselios College of Engineering and Technology, Thiruvananthapuram, Kerala, India.
Abstract
Due to the explosive growth of mobile data traffic and the shortage of spectral
resources, 5G networks are envisioned to have a densified heterogeneous
network (HetNet) architecture, combining multiple radio access technologies
(multi-RATs) into a single holistic network. Our aim is to address the technical
challenges of data traffic management, coordinated spectrum sharing in 5G
HetNets through the introduction of a programmable management platform
based on Non-Orthogonal Multiple Access (NOMA) and Successive
Interference Cancellation (SIC) method. It results in the cancellation of both
inter cellular and intra cellular interference. Also, the data rate at the receiver
side is obtained without interference and noise by reducing the power
consumption and increasing spectral efficiency. Interference has a large power
than its desired signal. So, at first, the interference signal is detected and SIC is
carried out to avoid interference.
Keywords: NOMA, Successive Interference Cancellation, Power
Optimization, 5G.
I. INTRODUCTION
As the interest for digital substance and administrations over the mobile networks keeps
on rising, same as the quantity of users/gadgets coming on the web, the present fourth
era (4G) of mobile networks area about or each their capacity limit. By 2022, around 9
billion mobile subscriptions, 8.3 billion mobile broadband memberships, and 6.2 billion
2 Swapna P.S. et al.
interesting mobile supporters are relied upon to be accessible. Mobile data traffic is
likewise anticipated to expand with this large amount of memberships, achieving
estimations of 71 Exabyte's for every month. The video applications drive the vast
majority of this high volume of data.
One of the fundamental upgrades coming about because of deploying the fifth
generation of mobile networks (5G) is therefore of high capacity in contrast with 4G. It
is normal that 5G networks will deal with traffic multiple times higher than 4G and 10
to multiple times quicker with connections. What's more, there are likewise necessities
for energy savings, near-zero latency and reduction in cost. 5G invites, among others,
the fast advancement of the shrewd urban areas, Internet of Things (IoT), associated
homes, self-governing drive, ultra-high definition video (UHD) and virtual reality.
Mobile communication frameworks like WiMAX, LTE (Long Term Evolution) and
LTE-Advanced are implemented in the fourth generation (4G), orthogonal multi-access
(OFDMA) or single carrier (SC)- FDMA dependent on orthogonal frequency division.
Orthogonal access is a sensible decision with a disentangled receiver configuration to
accomplish great framework throughput performance. Be that as it may, because of the
endlessly expanded requirement for high-volume administrations, such as, video
streaming, image transfer and cloud-based administrations, systems past 4G require
another mobile communication system with further upgrades in system throughput [1].
Different new innovations like the recent multiple access (MA) technologies, new
network designs and recent strategies for use of the spectrum are expected to meet the
prerequisites upgrading multiple access (MA) from the orthogonal to the non-
orthogonal is relied upon to be an encouraging methodology for the 5G framework
among the basic technologies.
To fathom the mobile data traffic, and to expand the capacity multiple times,
densification of cells, increment in bandwidth, and high spectral efficiency is required.
Because of use of 300 MHz to 3GHz, likewise called millimeter wave (mm wave), the
signals are influenced by increment in the loss of free isotropic space, loss through
attenuation and penetration during engendering, because of ingestion by downpour,
oxygen particles, and so on. Closed networking between macro cells and small cells
enables users to have a synchronous connection with small cells access points (APs)
and micro cell base station (BSs). A practical option is the wireless backhauling, which
gives innovation, augmentation of topographical coverage and extension of capacity to
totally utilize the heterogeneity of networks. A wireless self-backhauling utilizes a
similar frequency band for access links and backhaul (BH).
Different NOMA solutions can be partitioned fundamentally into two primary
methodologies. As opposed to NOMA power - domain multiplexing in the power
domain, NOMA code - domain multiplexing in the code domain. The NOMA code -
domain shares every single accessible resource (frequency/time, for example, the
fundamental division of a multi-access code (CDMA). Code-domain NOMA, then
again, utilizes user explicit transferring sequences that are cross sequences - correlation
sequences with a small correlation. [14]
Resource Management in 5G Heterogeneous Network 3
In this paper, the NOMA concept for spectrum efficiency upgrades of the future in
lower frequency groups for 5G system downlink is depicted. After that, the main
component technologies are exhibited. Power allocation and optimization, and receiver
design are discussed. Here we are augmenting the spectral efficiency and, in this way,
expanding the data rate and diminishing the power consumption. In the receiver design,
the interference is dropped by utilizing successive interference cancellation. Finally
plotting the channel capacity versus SINR of one antenna and comparing the bit error
rate of the proposed and the existing method.
II. PRINCIPLE OF NOMA
A. Concepts of NOMA
NOMA is an extremely encouraging methodology for future access to radio and can be
utilized for downlink just as uplink. In traditional 4G systems, as regular expansion of
OFDM, orthogonal frequency division multiple access (OFDMA) is utilized as a
characteristic augmentation of OFDM, orthogonal frequency division multiple access
(OFDMA) is utilized in customary 4G networks where data is assigned to a subset of
sub-carriers for every user. In NOMA, then again, the majority of the subcarriers may
be utilized by every user. Fig.1 shows the sharing of spectrum for OFDMA and NOMA
for two users.
Fig.1. Spectrum sharing for OFDMA and NOMA for two users. Courtesy [14]
In this paper, we introduce a transceiver concept. Here if a message is transmitted from
microcell to user then microcell is acting as a transmitter and user is acting as a receiver
and vice versa.
4 Swapna P.S. et al.
Fig.2. Transmission scenario for the NOMA-MIMO downlink system of 2-UE. Courtesy [13]
Fig.2 illustrates the downlink NOMA-MIMO transmission scenario for a 2-UE, where
UE1 is a cell- center (near user) user and UE2 is a cell-edge (far user) user. For example,
if user (UE) is to communicate with user1 (UE1), the former should sends its data to
microcell and then from microcell to user1 rather than direct communication between
users.
B. Benefits of NOMA
NOMA technology is utilized for improving spectrum efficiency and many benefits,
including but not limited to the following [2]:
It attains superior spectral efficiency by serving multiple users with the same
frequency resource and mitigating SIC interference, simultaneously.
It increases the number of users served at the same time and can therefore
support massive connectivity.
No need to expand the number of transmitting antennas: from the perspective of
expense and space requirements of macro cell and small cell deployments in the
practical network, it is very significant.
Very good backward compatibility with OFDMA and SC-FDMA: it is easy to
apply downlink NOMA and uplink SCFDMA on OFDMA.
NOMA have a very good affinity with multi - antenna technology: In order to improve
the system performance, NOMA can be effectively joined with beamforming and SU /
MU MIMO methods.
Resource Management in 5G Heterogeneous Network 5
III. SYSTEM MODEL
In this paper, we use downlink transmission for a NOMA network of microcell (base
stations) where M users and N microcells are present and the downlink bandwidth is
split into multiple subbands. Base Station conducts downlink transmission with
transmission power that is different for different users to multiple users, simultaneously,
in each subband. Different from OFDMA, where only one user is scheduled in a
subband, more than one user can be scheduled in a subband in NOMA. Let us consider
a subband with M users multiplexed simultaneously in the subband.
Fig.3. System Model
Fig.3 illustrates the system model, where L users and N antenna are used. The Rayleigh
channel and AWGN is added to the input data. The signal also consists of intercellular
and intracellular interference. Power allocation is done so as to maximize the spectral
efficiency. The transmission power for each microcell is decided based on the power
assignment ratio.
In the receiver design, SIC is done to remove interference and the channel data is
eliminated using Zero Force Equalization. Hard decision decoding is done to remove
noise data. Finally, the data is demodulated. The difference between the demodulated
data and input data gives the Bit Error Rate (BER). As BER decreases interferences
also reduces.
The signal received at the usern can be explained by,
where n, k∈[1, N] represents the user index, hn represents base station channel response
to usern and 𝑆𝑘 represents data symbol transmitted for userk. The inter- cell interference
and additive white Gaussian noise is observed at usern, and is denoted by In and nn.
6 Swapna P.S. et al.
A. Power Allocation and Optimization
The aim of power optimization is to increase the overall spectral efficiency and decrease
the power consumption with a constraint that the sum of power assignment ratio should
be equal to 1.We are using a BPSK modulated input data. The transmission power of k
microcell is determined by,
Pk=βk×PBS (2)
where βk∈(0, 1) denotes power assignment ratio of userk, PBS denotes transmission
power of BS.
While transmitting data through Rayleigh channel, it is affected by both intercellular
and intracellular interference as well additive white Gaussian noise. Then the received
signal is described as,
(3)
The power of the received signal is calculated using,
𝑷𝒁 𝟏
𝑵 ∑ | 𝒙|𝟐 (4)
where N is the length of the signal and x is the input.
The usern SINR after SIC post-processing can be calculated by
SINRPost =𝑃𝑛
∑ 𝛽𝐾+𝑛−1𝑘=1
1
𝑆𝐼𝑁𝑅𝑛
(5)
𝑆𝐼𝑁𝑅𝑛 =|hn|2 PBS / (NSB P1+N) (6)
SINRn denotes the SINR of user, when the total transmission power assigned to the
user and PI+N represents the total power of inter-cell interference and noise observed
at usern[2].
The SINR differs for both the existing and proposed method. For proposed method, the
power assignment β∈ (0,1) and the sum should be equal to 1. The channel capacity and
spectrum efficiency is calculated by:
Channel Capacity = B log(1+SINR) (7)
where, B is the bandwidth.
Spectrum Efficiency 𝐶ℎ𝑎𝑛𝑛𝑒𝑙 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦
𝐵𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ (8)
Resource Management in 5G Heterogeneous Network 7
Algorithm 1 Random Search algorithm
1. Initialize: {𝑖𝑘=0,eff=0;
2. Repeat:
1: evaluate fitness
2: if fitness greater than eff 3: evaluate β,C,𝑃𝑧
4: end
3. Find optimal power allocation value
B. Successive Interference Cancellation
Due to NOMA's non - orthogonality, the transmitter deliberately adds interference in
the power domain. SIC can be used to mitigate this interference. UEi's received signal
is as follows:
Yi =hiX +Wi (9) where hi denotes the complex channel coefficients between UEi and the base
station, and Wi(n) denotes the Gaussian noise including inter-cell interference affected
at UEi. The order that is optimal for decoding the signal received is in the order of the
increasing strength of signal (for example, the channel gain that is normalized by the
noise and inter- cell interference). In such manner, UEs are sorted out based on their
signal strength, with the goal that any UEn will first decode the signal that is the
strongest and isolates the desired signal from the combines signal received. To
illustrate the outline of SIC better, assume that we have two users, UE1 and UE2,
and at first, UE2 comes in the decoding order, so the signal of UE2 is the strongest
(more power). In the UE2 receiver, the decoding is as follows [3]:
The M2 message is decoded from Y2, and X1 is treated as noise. The interference
brought about by UE1 on UE2 ought not influence the performance of UE2 altogether,
as the power from this interference is probably going to be very much lower than the
signal desired. Now, this is substantial for the transmitter to be successful in power
allocation at the transmitter side.
The decoding method for UE1 is much more complicated and SIC is used here.
1. The message M2 is decoded from Y1, considering X1 as noise. The decoding
process for UE1 is more complex and SIC is utilized here. This step is conceivable
in light that UE1's channel gain is more than UE2's, so as long as UE2's rate is
within its recipient’s Shannon limits, it will likewise be inside the UE1 recipients'
limits.
8 Swapna P.S. et al.
2. Using an encoder, X2 is regenerated and with knowledge of h1 and P2, Y1 is
subtracted from h1√P2X2 and obtained:
Y’՛ = Y1− h1√P2X2 = h1√P1X1+Wi (10)
3. The message M1 is decoded from Y’.
C. Reconstruction of signal
The received signal is reconstructed by eliminating the channel data and noise. Using
Zero Force Equalization the channel data is removed. Hard decision decoding is done
to remove noise. The hard decision decoder makes a decision based on the threshold
value.
Fig.6 shows the Bit Error Rate plot. The Bit Error Rate is calculated by counting the
number of errors. If the error is minimum, then we can know that the signal received is
free of interference. By subtracting the reconstructed signal from the input signal, we
can determine the number of errors.
IV. PERFORMACE ANALYSIS AND SIMULATION PARAMETERS
A. Simulation parameters
The simulation parameters are listed below:
Table I . Simulation parameters
Parameters Value
Number of base antennas 4
Channel Rayleigh
Noise AWGN
Modulation BPSK
Bandwidth 10MHz
Power of the base band 60W
Resource Management in 5G Heterogeneous Network 9
Fig.4 Optimal Power Allocation Value Fig.4 depicts the power points that is optimal, which is obtained after power
optimization using random search algorithm with a constraint that the power
assignment ratio (β) lies within the range (0,1) and the sum of β should be equal to 1.
B. Performance analysis
In this paper, we compare the BER of the existing method and proposed method. Next,
we plotted the graph of channel capacity versus SINR for a single antenna.
Fig.5 Graph of Channel Capacity versus SINR
Fig.6 Bit Error Rate
10 Swapna P.S. et al.
V. CONCLUSION
Users are more concerned about efficient usage of available data and its usage without
outside interference. Using a SIC module, 5 G interference is eliminated. A new user
will not interfere with the normal connectivity here. It prevents intercellular interference
and intracellular interference. Therefore, the non - orthogonal multi - access system can
achieve significantly high cell output and cell edge output performance gains. This
increases the data rate by reducing energy consumption and increasing spectral
efficiency. Increased spectrum efficiency and system output therefore lead to a
difference in the channel between users, which is translated by non - orthogonal
multiplexing into multiplexing gain.
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